Overview

Dataset statistics

Number of variables24
Number of observations102825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 MiB
Average record size in memory200.0 B

Variable types

Numeric16
Categorical8

Alerts

id is uniformly distributedUniform
id has unique valuesUnique
Inflight wifi service has 3075 (3.0%) zerosZeros
Departure/Arrival time convenient has 5259 (5.1%) zerosZeros
Ease of Online booking has 4443 (4.3%) zerosZeros
Online boarding has 2428 (2.4%) zerosZeros
Departure Delay in Minutes has 58649 (57.0%) zerosZeros
Arrival Delay in Minutes has 58135 (56.5%) zerosZeros

Reproduction

Analysis started2023-02-21 16:53:22.461516
Analysis finished2023-02-21 16:53:53.214424
Duration30.75 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM
UNIQUE

Distinct102825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64979.966
Minimum1
Maximum129880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:53.288454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6601.2
Q132590
median64939
Q397431
95-th percentile123447.4
Maximum129880
Range129879
Interquartile range (IQR)64841

Descriptive statistics

Standard deviation37471.265
Coefficient of variation (CV)0.57665873
Kurtosis-1.1984264
Mean64979.966
Median Absolute Deviation (MAD)32424
Skewness0.001639048
Sum6.681565 × 109
Variance1.4040957 × 109
MonotonicityNot monotonic
2023-02-21T11:53:53.396074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70172 1
 
< 0.1%
60921 1
 
< 0.1%
118574 1
 
< 0.1%
23529 1
 
< 0.1%
16272 1
 
< 0.1%
58438 1
 
< 0.1%
2352 1
 
< 0.1%
65908 1
 
< 0.1%
67057 1
 
< 0.1%
18481 1
 
< 0.1%
Other values (102815) 102815
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
129880 1
< 0.1%
129879 1
< 0.1%
129878 1
< 0.1%
129875 1
< 0.1%
129874 1
< 0.1%
129873 1
< 0.1%
129871 1
< 0.1%
129870 1
< 0.1%
129869 1
< 0.1%
129867 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Female
52169 
Male
50656 

Length

Max length6
Median length6
Mean length5.0147143
Min length4

Characters and Unicode

Total characters515638
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 52169
50.7%
Male 50656
49.3%

Length

2023-02-21T11:53:53.494125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:53.592159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female 52169
50.7%
male 50656
49.3%

Most occurring characters

ValueCountFrequency (%)
e 154994
30.1%
a 102825
19.9%
l 102825
19.9%
F 52169
 
10.1%
m 52169
 
10.1%
M 50656
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 412813
80.1%
Uppercase Letter 102825
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 154994
37.5%
a 102825
24.9%
l 102825
24.9%
m 52169
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 52169
50.7%
M 50656
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 515638
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 154994
30.1%
a 102825
19.9%
l 102825
19.9%
F 52169
 
10.1%
m 52169
 
10.1%
M 50656
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 515638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 154994
30.1%
a 102825
19.9%
l 102825
19.9%
F 52169
 
10.1%
m 52169
 
10.1%
M 50656
 
9.8%

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Loyal Customer
84003 
disloyal Customer
18822 

Length

Max length17
Median length14
Mean length14.549147
Min length14

Characters and Unicode

Total characters1496016
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowdisloyal Customer
3rd rowLoyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 84003
81.7%
disloyal Customer 18822
 
18.3%

Length

2023-02-21T11:53:53.665163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:53.744606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 102825
50.0%
loyal 84003
40.8%
disloyal 18822
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 205650
13.7%
l 121647
 
8.1%
s 121647
 
8.1%
y 102825
 
6.9%
a 102825
 
6.9%
102825
 
6.9%
C 102825
 
6.9%
u 102825
 
6.9%
t 102825
 
6.9%
m 102825
 
6.9%
Other values (5) 327297
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1206363
80.6%
Uppercase Letter 186828
 
12.5%
Space Separator 102825
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 205650
17.0%
l 121647
10.1%
s 121647
10.1%
y 102825
8.5%
a 102825
8.5%
u 102825
8.5%
t 102825
8.5%
m 102825
8.5%
e 102825
8.5%
r 102825
8.5%
Other values (2) 37644
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 102825
55.0%
L 84003
45.0%
Space Separator
ValueCountFrequency (%)
102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1393191
93.1%
Common 102825
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 205650
14.8%
l 121647
8.7%
s 121647
8.7%
y 102825
7.4%
a 102825
7.4%
C 102825
7.4%
u 102825
7.4%
t 102825
7.4%
m 102825
7.4%
e 102825
7.4%
Other values (4) 224472
16.1%
Common
ValueCountFrequency (%)
102825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1496016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 205650
13.7%
l 121647
 
8.1%
s 121647
 
8.1%
y 102825
 
6.9%
a 102825
 
6.9%
102825
 
6.9%
C 102825
 
6.9%
u 102825
 
6.9%
t 102825
 
6.9%
m 102825
 
6.9%
Other values (5) 327297
21.9%

Age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.377
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:53.823071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.103129
Coefficient of variation (CV)0.38355204
Kurtosis-0.71989283
Mean39.377
Median Absolute Deviation (MAD)12
Skewness-0.0047534008
Sum4048940
Variance228.1045
MonotonicityNot monotonic
2023-02-21T11:53:53.914954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2938
 
2.9%
25 2769
 
2.7%
40 2544
 
2.5%
44 2462
 
2.4%
42 2431
 
2.4%
41 2427
 
2.4%
22 2338
 
2.3%
45 2319
 
2.3%
23 2318
 
2.3%
47 2309
 
2.2%
Other values (65) 77970
75.8%
ValueCountFrequency (%)
7 557
0.5%
8 634
0.6%
9 682
0.7%
10 670
0.7%
11 667
0.6%
12 630
0.6%
13 619
0.6%
14 701
0.7%
15 807
0.8%
16 884
0.9%
ValueCountFrequency (%)
85 17
 
< 0.1%
80 75
 
0.1%
79 40
 
< 0.1%
78 30
 
< 0.1%
77 85
0.1%
76 45
 
< 0.1%
75 60
 
0.1%
74 44
 
< 0.1%
73 49
 
< 0.1%
72 198
0.2%

Type of Travel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Business travel
70897 
Personal Travel
31928 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1542375
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel 70897
68.9%
Personal Travel 31928
31.1%

Length

2023-02-21T11:53:54.002909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:54.075163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
travel 102825
50.0%
business 70897
34.5%
personal 31928
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 244619
15.9%
e 205650
13.3%
r 134753
8.7%
a 134753
8.7%
l 134753
8.7%
n 102825
6.7%
102825
6.7%
v 102825
6.7%
B 70897
 
4.6%
u 70897
 
4.6%
Other values (5) 237578
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1304797
84.6%
Uppercase Letter 134753
 
8.7%
Space Separator 102825
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 244619
18.7%
e 205650
15.8%
r 134753
10.3%
a 134753
10.3%
l 134753
10.3%
n 102825
7.9%
v 102825
7.9%
u 70897
 
5.4%
i 70897
 
5.4%
t 70897
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 70897
52.6%
P 31928
23.7%
T 31928
23.7%
Space Separator
ValueCountFrequency (%)
102825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1439550
93.3%
Common 102825
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 244619
17.0%
e 205650
14.3%
r 134753
9.4%
a 134753
9.4%
l 134753
9.4%
n 102825
7.1%
v 102825
7.1%
B 70897
 
4.9%
u 70897
 
4.9%
i 70897
 
4.9%
Other values (4) 166681
11.6%
Common
ValueCountFrequency (%)
102825
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 244619
15.9%
e 205650
13.3%
r 134753
8.7%
a 134753
8.7%
l 134753
8.7%
n 102825
6.7%
102825
6.7%
v 102825
6.7%
B 70897
 
4.6%
u 70897
 
4.6%
Other values (5) 237578
15.4%

Class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Business
49125 
Eco
46278 
Eco Plus
7422 

Length

Max length8
Median length8
Mean length5.7496718
Min length3

Characters and Unicode

Total characters591210
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco Plus
2nd rowBusiness
3rd rowBusiness
4th rowBusiness
5th rowBusiness

Common Values

ValueCountFrequency (%)
Business 49125
47.8%
Eco 46278
45.0%
Eco Plus 7422
 
7.2%

Length

2023-02-21T11:53:54.142133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:54.220109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
eco 53700
48.7%
business 49125
44.6%
plus 7422
 
6.7%

Most occurring characters

ValueCountFrequency (%)
s 154797
26.2%
u 56547
 
9.6%
E 53700
 
9.1%
c 53700
 
9.1%
o 53700
 
9.1%
B 49125
 
8.3%
i 49125
 
8.3%
n 49125
 
8.3%
e 49125
 
8.3%
7422
 
1.3%
Other values (2) 14844
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 473541
80.1%
Uppercase Letter 110247
 
18.6%
Space Separator 7422
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 154797
32.7%
u 56547
 
11.9%
c 53700
 
11.3%
o 53700
 
11.3%
i 49125
 
10.4%
n 49125
 
10.4%
e 49125
 
10.4%
l 7422
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 53700
48.7%
B 49125
44.6%
P 7422
 
6.7%
Space Separator
ValueCountFrequency (%)
7422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 583788
98.7%
Common 7422
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 154797
26.5%
u 56547
 
9.7%
E 53700
 
9.2%
c 53700
 
9.2%
o 53700
 
9.2%
B 49125
 
8.4%
i 49125
 
8.4%
n 49125
 
8.4%
e 49125
 
8.4%
P 7422
 
1.3%
Common
ValueCountFrequency (%)
7422
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 591210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 154797
26.2%
u 56547
 
9.6%
E 53700
 
9.1%
c 53700
 
9.1%
o 53700
 
9.1%
B 49125
 
8.3%
i 49125
 
8.3%
n 49125
 
8.3%
e 49125
 
8.3%
7422
 
1.3%
Other values (2) 14844
 
2.5%

Flight Distance
Real number (ℝ)

Distinct3798
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1184.6871
Minimum31
Maximum4243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.302406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1413
median841
Q31735
95-th percentile3372
Maximum4243
Range4212
Interquartile range (IQR)1322

Descriptive statistics

Standard deviation992.2101
Coefficient of variation (CV)0.83752926
Kurtosis0.25325863
Mean1184.6871
Median Absolute Deviation (MAD)514
Skewness1.1076961
Sum1.2181545 × 108
Variance984480.88
MonotonicityNot monotonic
2023-02-21T11:53:54.398431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 654
 
0.6%
594 395
 
0.4%
404 390
 
0.4%
862 368
 
0.4%
2475 361
 
0.4%
447 358
 
0.3%
236 350
 
0.3%
399 330
 
0.3%
308 329
 
0.3%
192 328
 
0.3%
Other values (3788) 98962
96.2%
ValueCountFrequency (%)
31 8
 
< 0.1%
56 8
 
< 0.1%
67 124
0.1%
73 58
0.1%
74 30
 
< 0.1%
76 1
 
< 0.1%
77 41
 
< 0.1%
78 30
 
< 0.1%
80 2
 
< 0.1%
82 7
 
< 0.1%
ValueCountFrequency (%)
4243 17
< 0.1%
4000 11
< 0.1%
3999 5
 
< 0.1%
3998 8
< 0.1%
3997 9
< 0.1%
3996 8
< 0.1%
3995 6
 
< 0.1%
3994 6
 
< 0.1%
3993 13
< 0.1%
3992 6
 
< 0.1%

Inflight wifi service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7302018
Minimum0
Maximum5
Zeros3075
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.476121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3283094
Coefficient of variation (CV)0.48652424
Kurtosis-0.84688829
Mean2.7302018
Median Absolute Deviation (MAD)1
Skewness0.040054423
Sum280733
Variance1.7644058
MonotonicityNot monotonic
2023-02-21T11:53:54.539632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 25583
24.9%
2 25553
24.9%
4 19592
19.1%
1 17650
17.2%
5 11372
11.1%
0 3075
 
3.0%
ValueCountFrequency (%)
0 3075
 
3.0%
1 17650
17.2%
2 25553
24.9%
3 25583
24.9%
4 19592
19.1%
5 11372
11.1%
ValueCountFrequency (%)
5 11372
11.1%
4 19592
19.1%
3 25583
24.9%
2 25553
24.9%
1 17650
17.2%
0 3075
 
3.0%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0595381
Minimum0
Maximum5
Zeros5259
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.601312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5257378
Coefficient of variation (CV)0.49868241
Kurtosis-1.0388839
Mean3.0595381
Median Absolute Deviation (MAD)1
Skewness-0.33386215
Sum314597
Variance2.3278759
MonotonicityNot monotonic
2023-02-21T11:53:54.664236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 25255
24.6%
5 22179
21.6%
3 17773
17.3%
2 17004
16.5%
1 15355
14.9%
0 5259
 
5.1%
ValueCountFrequency (%)
0 5259
 
5.1%
1 15355
14.9%
2 17004
16.5%
3 17773
17.3%
4 25255
24.6%
5 22179
21.6%
ValueCountFrequency (%)
5 22179
21.6%
4 25255
24.6%
3 17773
17.3%
2 17004
16.5%
1 15355
14.9%
0 5259
 
5.1%

Ease of Online booking
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7569463
Minimum0
Maximum5
Zeros4443
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.726569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3992376
Coefficient of variation (CV)0.50753169
Kurtosis-0.91099434
Mean2.7569463
Median Absolute Deviation (MAD)1
Skewness-0.018553027
Sum283483
Variance1.9578659
MonotonicityNot monotonic
2023-02-21T11:53:54.789594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 24198
23.5%
2 23742
23.1%
4 19369
18.8%
1 17359
16.9%
5 13714
13.3%
0 4443
 
4.3%
ValueCountFrequency (%)
0 4443
 
4.3%
1 17359
16.9%
2 23742
23.1%
3 24198
23.5%
4 19369
18.8%
5 13714
13.3%
ValueCountFrequency (%)
5 13714
13.3%
4 19369
18.8%
3 24198
23.5%
2 23742
23.1%
1 17359
16.9%
0 4443
 
4.3%

Gate location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9760759
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.853536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2778111
Coefficient of variation (CV)0.42936106
Kurtosis-1.0308461
Mean2.9760759
Median Absolute Deviation (MAD)1
Skewness-0.058018435
Sum306015
Variance1.6328012
MonotonicityNot monotonic
2023-02-21T11:53:54.916143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28270
27.5%
4 24153
23.5%
2 19272
18.7%
1 17399
16.9%
5 13730
13.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 17399
16.9%
2 19272
18.7%
3 28270
27.5%
4 24153
23.5%
5 13730
13.4%
ValueCountFrequency (%)
5 13730
13.4%
4 24153
23.5%
3 28270
27.5%
2 19272
18.7%
1 17399
16.9%
0 1
 
< 0.1%

Food and drink
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2040263
Minimum0
Maximum5
Zeros77
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:54.976177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3290101
Coefficient of variation (CV)0.41479377
Kurtosis-1.1495426
Mean3.2040263
Median Absolute Deviation (MAD)1
Skewness-0.15022514
Sum329454
Variance1.7662679
MonotonicityNot monotonic
2023-02-21T11:53:55.039331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 24083
23.4%
5 22141
21.5%
3 22069
21.5%
2 21755
21.2%
1 12700
12.4%
0 77
 
0.1%
ValueCountFrequency (%)
0 77
 
0.1%
1 12700
12.4%
2 21755
21.2%
3 22069
21.5%
4 24083
23.4%
5 22141
21.5%
ValueCountFrequency (%)
5 22141
21.5%
4 24083
23.4%
3 22069
21.5%
2 21755
21.2%
1 12700
12.4%
0 77
 
0.1%

Online boarding
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2501629
Minimum0
Maximum5
Zeros2428
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:55.101412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.349176
Coefficient of variation (CV)0.41511028
Kurtosis-0.69673118
Mean3.2501629
Median Absolute Deviation (MAD)1
Skewness-0.45554934
Sum334198
Variance1.820276
MonotonicityNot monotonic
2023-02-21T11:53:55.165473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30471
29.6%
3 21610
21.0%
5 20460
19.9%
2 17328
16.9%
1 10528
 
10.2%
0 2428
 
2.4%
ValueCountFrequency (%)
0 2428
 
2.4%
1 10528
 
10.2%
2 17328
16.9%
3 21610
21.0%
4 30471
29.6%
5 20460
19.9%
ValueCountFrequency (%)
5 20460
19.9%
4 30471
29.6%
3 21610
21.0%
2 17328
16.9%
1 10528
 
10.2%
0 2428
 
2.4%

Seat comfort
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4
31470 
5
26214 
3
18497 
2
14721 
1
11923 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row5
4th row2
5th row5

Common Values

ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Length

2023-02-21T11:53:55.235127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:55.312456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Most occurring characters

ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 31470
30.6%
5 26214
25.5%
3 18497
18.0%
2 14721
14.3%
1 11923
 
11.6%

Inflight entertainment
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3597763
Minimum0
Maximum5
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:55.385492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3335675
Coefficient of variation (CV)0.39692151
Kurtosis-1.0617288
Mean3.3597763
Median Absolute Deviation (MAD)1
Skewness-0.36651827
Sum345469
Variance1.7784023
MonotonicityNot monotonic
2023-02-21T11:53:55.449048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 29144
28.3%
5 25021
24.3%
3 18830
18.3%
2 17482
17.0%
1 12334
12.0%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 12334
12.0%
2 17482
17.0%
3 18830
18.3%
4 29144
28.3%
5 25021
24.3%
ValueCountFrequency (%)
5 25021
24.3%
4 29144
28.3%
3 18830
18.3%
2 17482
17.0%
1 12334
12.0%
0 14
 
< 0.1%

On-board service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3847411
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:55.511066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2878225
Coefficient of variation (CV)0.3804789
Kurtosis-0.88766114
Mean3.3847411
Median Absolute Deviation (MAD)1
Skewness-0.42377352
Sum348036
Variance1.6584869
MonotonicityNot monotonic
2023-02-21T11:53:55.576080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30637
29.8%
5 23425
22.8%
3 22579
22.0%
2 14445
14.0%
1 11736
 
11.4%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11736
 
11.4%
2 14445
14.0%
3 22579
22.0%
4 30637
29.8%
5 23425
22.8%
ValueCountFrequency (%)
5 23425
22.8%
4 30637
29.8%
3 22579
22.0%
2 14445
14.0%
1 11736
 
11.4%
0 3
 
< 0.1%

Leg room service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3476003
Minimum0
Maximum5
Zeros472
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:55.640108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3172989
Coefficient of variation (CV)0.39350544
Kurtosis-0.98616417
Mean3.3476003
Median Absolute Deviation (MAD)1
Skewness-0.34619646
Sum344217
Variance1.7352765
MonotonicityNot monotonic
2023-02-21T11:53:55.703135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 28381
27.6%
5 24400
23.7%
3 19849
19.3%
2 19423
18.9%
1 10300
 
10.0%
0 472
 
0.5%
ValueCountFrequency (%)
0 472
 
0.5%
1 10300
 
10.0%
2 19423
18.9%
3 19849
19.3%
4 28381
27.6%
5 24400
23.7%
ValueCountFrequency (%)
5 24400
23.7%
4 28381
27.6%
3 19849
19.3%
2 19423
18.9%
1 10300
 
10.0%
0 472
 
0.5%

Baggage handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4
36962 
5
26849 
3
20401 
2
11420 
1
7193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Length

2023-02-21T11:53:56.172140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:56.249171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Most occurring characters

ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 36962
35.9%
5 26849
26.1%
3 20401
19.8%
2 11420
 
11.1%
1 7193
 
7.0%

Checkin service
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
4
28761 
3
28248 
5
20362 
1
12737 
2
12717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102825
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row4
4th row1
5th row3

Common Values

ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Length

2023-02-21T11:53:56.328230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:56.410203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Most occurring characters

ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 102825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 28761
28.0%
3 28248
27.5%
5 20362
19.8%
1 12737
12.4%
2 12717
12.4%

Inflight service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6450571
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:56.481212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1737548
Coefficient of variation (CV)0.32201272
Kurtosis-0.34747489
Mean3.6450571
Median Absolute Deviation (MAD)1
Skewness-0.6950352
Sum374803
Variance1.3777002
MonotonicityNot monotonic
2023-02-21T11:53:56.547385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 37676
36.6%
5 26922
26.2%
3 19977
19.4%
2 11311
 
11.0%
1 6936
 
6.7%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 6936
 
6.7%
2 11311
 
11.0%
3 19977
19.4%
4 37676
36.6%
5 26922
26.2%
ValueCountFrequency (%)
5 26922
26.2%
4 37676
36.6%
3 19977
19.4%
2 11311
 
11.0%
1 6936
 
6.7%
0 3
 
< 0.1%

Cleanliness
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2862047
Minimum0
Maximum5
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:56.613440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3116768
Coefficient of variation (CV)0.3991464
Kurtosis-1.0113409
Mean3.2862047
Median Absolute Deviation (MAD)1
Skewness-0.29971119
Sum337904
Variance1.720496
MonotonicityNot monotonic
2023-02-21T11:53:56.677374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 26885
26.1%
3 24376
23.7%
5 22432
21.8%
2 15955
15.5%
1 13166
12.8%
0 11
 
< 0.1%
ValueCountFrequency (%)
0 11
 
< 0.1%
1 13166
12.8%
2 15955
15.5%
3 24376
23.7%
4 26885
26.1%
5 22432
21.8%
ValueCountFrequency (%)
5 22432
21.8%
4 26885
26.1%
3 24376
23.7%
2 15955
15.5%
1 13166
12.8%
0 11
 
< 0.1%
Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.32036
Minimum0
Maximum215
Zeros58649
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:56.762749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile69
Maximum215
Range215
Interquartile range (IQR)12

Descriptive statistics

Standard deviation26.688097
Coefficient of variation (CV)2.1661784
Kurtosis12.452258
Mean12.32036
Median Absolute Deviation (MAD)0
Skewness3.2801619
Sum1266841
Variance712.25451
MonotonicityNot monotonic
2023-02-21T11:53:56.857441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58649
57.0%
1 2947
 
2.9%
2 2272
 
2.2%
3 2007
 
2.0%
4 1852
 
1.8%
5 1692
 
1.6%
6 1515
 
1.5%
7 1392
 
1.4%
8 1295
 
1.3%
9 1254
 
1.2%
Other values (204) 27950
27.2%
ValueCountFrequency (%)
0 58649
57.0%
1 2947
 
2.9%
2 2272
 
2.2%
3 2007
 
2.0%
4 1852
 
1.8%
5 1692
 
1.6%
6 1515
 
1.5%
7 1392
 
1.4%
8 1295
 
1.3%
9 1254
 
1.2%
ValueCountFrequency (%)
215 1
 
< 0.1%
214 2
 
< 0.1%
212 5
< 0.1%
211 2
 
< 0.1%
210 2
 
< 0.1%
209 4
< 0.1%
207 1
 
< 0.1%
206 2
 
< 0.1%
205 3
< 0.1%
204 3
< 0.1%

Arrival Delay in Minutes
Real number (ℝ)

Distinct320
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.721232
Minimum0
Maximum242
Zeros58135
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-02-21T11:53:56.954425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile70
Maximum242
Range242
Interquartile range (IQR)12

Descriptive statistics

Standard deviation27.259741
Coefficient of variation (CV)2.1428539
Kurtosis12.765626
Mean12.721232
Median Absolute Deviation (MAD)0
Skewness3.3008178
Sum1308060.6
Variance743.09349
MonotonicityNot monotonic
2023-02-21T11:53:57.057446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58135
56.5%
1 2211
 
2.2%
2 2061
 
2.0%
3 1952
 
1.9%
4 1906
 
1.9%
5 1657
 
1.6%
6 1616
 
1.6%
7 1481
 
1.4%
8 1394
 
1.4%
9 1264
 
1.2%
Other values (310) 29148
28.3%
ValueCountFrequency (%)
0 58135
56.5%
0.7224441073 116
 
0.1%
1 2211
 
2.2%
1.702664742 9
 
< 0.1%
2 2061
 
2.0%
2.682885377 8
 
< 0.1%
3 1952
 
1.9%
3.663106011 3
 
< 0.1%
4 1906
 
1.9%
4.643326646 9
 
< 0.1%
ValueCountFrequency (%)
242 1
 
< 0.1%
237 1
 
< 0.1%
229 2
 
< 0.1%
227 1
 
< 0.1%
226 1
 
< 0.1%
225 1
 
< 0.1%
224 3
< 0.1%
222 3
< 0.1%
221 3
< 0.1%
219 6
< 0.1%

satisfaction
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
neutral or dissatisfied
58226 
satisfied
44599 

Length

Max length23
Median length23
Mean length16.927683
Min length9

Characters and Unicode

Total characters1740589
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowneutral or dissatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied 58226
56.6%
satisfied 44599
43.4%

Length

2023-02-21T11:53:57.147615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T11:53:57.223530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral 58226
26.6%
or 58226
26.6%
dissatisfied 58226
26.6%
satisfied 44599
20.3%

Most occurring characters

ValueCountFrequency (%)
i 263876
15.2%
s 263876
15.2%
e 161051
9.3%
t 161051
9.3%
a 161051
9.3%
d 161051
9.3%
r 116452
6.7%
116452
6.7%
f 102825
 
5.9%
n 58226
 
3.3%
Other values (3) 174678
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1624137
93.3%
Space Separator 116452
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 263876
16.2%
s 263876
16.2%
e 161051
9.9%
t 161051
9.9%
a 161051
9.9%
d 161051
9.9%
r 116452
7.2%
f 102825
 
6.3%
n 58226
 
3.6%
u 58226
 
3.6%
Other values (2) 116452
7.2%
Space Separator
ValueCountFrequency (%)
116452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1624137
93.3%
Common 116452
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 263876
16.2%
s 263876
16.2%
e 161051
9.9%
t 161051
9.9%
a 161051
9.9%
d 161051
9.9%
r 116452
7.2%
f 102825
 
6.3%
n 58226
 
3.6%
u 58226
 
3.6%
Other values (2) 116452
7.2%
Common
ValueCountFrequency (%)
116452
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1740589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 263876
15.2%
s 263876
15.2%
e 161051
9.3%
t 161051
9.3%
a 161051
9.3%
d 161051
9.3%
r 116452
6.7%
116452
6.7%
f 102825
 
5.9%
n 58226
 
3.3%
Other values (3) 174678
10.0%

Interactions

2023-02-21T11:53:50.647613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:23.934801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.666686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.502670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.173034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.039589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.825133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.585217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.546456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.219355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.867067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.796322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.474216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.166579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.961240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.004360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.753678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.054378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.774236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.608331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.280269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.152300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.932218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.692443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.655477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.325907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.972358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.900414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.583268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.281173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.069352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.120356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.861327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.178051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.883099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.716280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.388877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.267383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.041335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.002400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.763101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.433263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.078693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.007451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.692400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.391211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.196601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.226580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.961764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.289077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.102168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.817382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.490513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.374260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.144689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.106411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.880417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.533312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.180067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.108513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.795635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.493269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.302829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.327811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.065459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.398154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.208035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.921313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.594392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.483044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.250407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.215091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.986924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.635400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.283148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.212460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.901262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.596568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.413421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.426258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.167506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.505042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.316116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.026447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.700415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.595933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.370643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.324131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.091860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.739586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.390260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.317512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.003547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.701474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.526030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.528184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.272182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.612396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.428234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.131496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.953499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.714959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.486545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.461226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.195182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.842186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.496351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.423311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.106567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.806335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.633120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.625242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.373308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.718167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.537288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.236290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.057034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.845552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.596106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.567188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.295141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.944209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.602217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.527913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.213168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.909437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.735164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.724670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.476417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.822554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.646275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.340293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.164198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:31.957484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.702131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.671783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.398178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.046380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:40.950295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.631531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.317239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.026416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.839325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.821285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.580510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:24.926668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.753758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.443087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.275558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.067044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.808964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.797414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.502201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.146382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.057215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.736129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.421418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.156506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:47.944395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:49.919588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.684203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.032467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.859988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.548022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.385079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.174107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:33.913241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:35.909337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.606225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.250298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.163253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.841530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.526457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.308081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.365695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.018540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.786149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.140050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:26.964290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.652145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.493119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.283146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.032539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.013900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.708076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.352219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.268777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:42.945871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.629502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.417828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.472056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.117252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.889187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.250077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.077349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.755278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.600195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.390222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.144166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.116386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.809993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.456317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.371171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.051186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.745098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.521876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.577327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.223319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:51.991566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.355111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.183431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.859651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.710263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.498409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.258273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.221348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:37.912854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.557363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.479217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.155566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.847585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.633302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.689404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.341197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:52.095357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.460466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.290038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:28.963657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.818357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.609961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.367505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.326431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.014447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.658201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.582735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.261631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:44.949182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.750131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.793453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.445297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:52.192159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:25.562459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:27.395599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:29.068545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:30.925415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:32.717568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:34.474835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:36.429417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:38.115097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:39.761320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:41.690366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:43.367650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:45.053222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:46.854168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:48.896563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T11:53:50.545462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-02-21T11:53:52.376422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-21T11:53:52.785987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
070172MaleLoyal Customer13Personal TravelEco Plus4603431535543445525.018.0neutral or dissatisfied
15047Maledisloyal Customer25Business travelBusiness235323313111531411.06.0neutral or dissatisfied
2110028FemaleLoyal Customer26Business travelBusiness1142222255554344450.00.0satisfied
324026FemaleLoyal Customer25Business travelBusiness5622555222225314211.09.0neutral or dissatisfied
4119299MaleLoyal Customer61Business travelBusiness214333345533443330.00.0satisfied
5111157FemaleLoyal Customer26Personal TravelEco1180342112113444410.00.0neutral or dissatisfied
682113MaleLoyal Customer47Personal TravelEco1276242322223343529.023.0neutral or dissatisfied
796462FemaleLoyal Customer52Business travelBusiness2035434455555554544.00.0satisfied
879485FemaleLoyal Customer41Business travelBusiness853122243311214120.00.0neutral or dissatisfied
965725Maledisloyal Customer20Business travelEco1061333423322344320.00.0neutral or dissatisfied
idGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
10389486549MaleLoyal Customer26Business travelBusiness7124444555534434517.026.0satisfied
10389566030Femaledisloyal Customer24Business travelEco10551112111133554113.010.0neutral or dissatisfied
10389671445MaleLoyal Customer57Business travelEco867455544443431340.00.0neutral or dissatisfied
103897102203FemaleLoyal Customer60Business travelBusiness1599555555444444449.07.0satisfied
10389860666MaleLoyal Customer50Personal TravelEco1620313423224342420.00.0neutral or dissatisfied
10389994171Femaledisloyal Customer23Business travelEco192212322223142323.00.0neutral or dissatisfied
10390073097MaleLoyal Customer49Business travelBusiness2347444424555555540.00.0satisfied
10390168825Maledisloyal Customer30Business travelBusiness1995111341543245547.014.0neutral or dissatisfied
10390254173Femaledisloyal Customer22Business travelEco1000111511114515410.00.0neutral or dissatisfied
10390362567MaleLoyal Customer27Business travelBusiness1723133311111144310.00.0neutral or dissatisfied